让大模型根据地理位置切换答案，解决本地知识混淆问题
MAPLE: Metadata Conditioned LLM Pretraining for Locale-Aware Question Answering
- 用文档地理元数据（来源网址、国家、洲）训练模型，实现位置感知
- 在18700条跨地区新闻问答上，模型准确率提升，能正确切换答案
- 适合需要地域敏感推理的应用，如本地新闻、政策问答
大语言模型虽能记住多个地区特有的事实，但在地缘变化时却无法选择正确答案，常默认输出全局主导的答案。我们将其定义为局部知识消歧问题，并构建了LocalNewsQA——一个包含18,700个条目的英文新闻基准，通过同一问题在两个不同地区的对比，评估模型是否随地缘变化切换答案。我们提出MAPLE，一种基于解码器的可控模型家族，使用训练语料中已有的文档级地理元数据（来源URL、国家、洲）进行预训练，与不带元数据的对照组在相同数据量、架构和优化条件下对比。在10亿和30亿参数规模下，固定推理时元数据，带有元数据预训练的MAPLE可实现显著的答案切换行为，提升依赖地缘的问答准确率。消融实验与外部基准测试进一步表明，地缘条件预测受益于预训练阶段学到的地理来源信息，且这种优势随模型规模增大而增强。
原文摘要 · Abstract (English)
Large language models can memorize competing locale-specific facts yet fail to select among them when the locale changes, defaulting instead to a single globally dominant answer. We formalize this as localized knowledge disambiguation and introduce LocalNewsQA, an 18,700-item English-news benchmark that pairs the same question across two locales and scores whether a model actually switches its answer when the locale changes. We also introduce MAPLE, a controlled family of decoder-only models pretrained with document-level geographic metadata (source URL, country, and continent) already present in the training corpus, and compare it to metadata-free controls trained on identical data with the same token budget, architecture, and optimization. In controlled experiments at 1B and 3B, with inference-time metadata fixed, pretraining with metadata in MAPLE produces measurable switching and improves accuracy on questions whose correct answer depends on locale. Ablations and external-benchmark evaluations further suggest that locale-conditioned prediction benefits from geographic provenance learned during pretraining and that these benefits strengthen at larger model sizes.
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